Large System Analysis of Cognitive Radio Network via Partially-Projected Regularized Zero-Forcing Precoding

TL;DR

This paper develops a large system analysis for cognitive radio networks using partially-projected regularized zero-forcing precoding, deriving deterministic expressions to optimize system performance without extensive simulations.

Contribution

It introduces a deterministic equivalent for the ergodic sum-rate of PP-RZF precoding, simplifying parameter optimization in large-scale cognitive radio networks.

Findings

Deterministic expression for ergodic sum-rate derived

Optimal parameters can be efficiently computed without Monte-Carlo simulations

Insights into interference control and system performance obtained

Abstract

In this paper, we consider a cognitive radio (CR) network in which a secondary multiantenna base station (BS) attempts to communicate with multiple secondary users (SUs) using the radio frequency spectrum that is originally allocated to multiple primary users (PUs). Here, we employ partially-projected regularized zero-forcing (PP-RZF) precoding to control the amount of interference at the PUs and to minimize inter-SUs interference. The PP-RZF precoding partially projects the channels of the SUs into the null space of the channels from the secondary BS to the PUs. The regularization parameter and the projection control parameter are used to balance the transmissions to the PUs and the SUs. However, the search for the optimal parameters, which can maximize the ergodic sum-rate of the CR network, is a demanding process because it involves Monte-Carlo averaging. Then, we derive a deterministic expression for the ergodic sum-rate achieved by the PP-RZF precoding using recent advancements in large dimensional random matrix theory. The deterministic equivalent enables us to efficiently determine the two critical parameters in the PP-RZF precoding because no Monte-Carlo averaging is required. Several insights are also obtained through the analysis.